Characterization of probability distribution convergence in Wasserstein distance by $L^p$-quantization error function

Abstract

We establish conditions to characterize probability measures by their $L^p$-quantization error functions in both ${\mathbb{R}}^d$ and Hilbert settings. This characterization is two-fold: static (identity of two distributions) and dynamic (convergence for the $L^p$-Wasserstein distance). We first propose a criterion on the quantization level $N$, valid for any norm on ${\mathbb{R}}^d$ and any order $p$ based on a geometrical approach involving the Voronoï diagram. Then, we prove that in the $L^2$-case on a (separable) Hilbert space, the condition on the level $N$ can be reduced to $N=2$, which is optimal. More quantization based characterization cases on dimension 1 and a discussion of the completeness of a distance defined by the quantization error function can be found in the end of this paper.